Deep learning techniques for biomedical data processing
What is it about?
The paper is a survey on DL approaches used for biomedical data processing. First, the fundamental key concepts of DL architectures will be introduced, with particular reference to neural networks for structured data, convolutional neural networks, generative adversarial models, and siamese architectures. Subsequently, their applicability for the analysis of different types of biomedical data will be shown, in areas ranging from imaging diagnostics to the understanding of the characteristics underlying the process of transcription and translation of our genetic code, up to the discovery of new drugs. Finally, the prospects and future expectations of DL applications to biomedical data will be discussed.
Why is it important?
The paper is important because it shows the vast applicability of deep learning in both the biochemical and medical field.